(1) Field of the Invention
The present invention relates generally to data integration and decision support systems based on fuzzy logic and, more specifically, to a fuzzy logic based system and method for information processing that is capable of handling uncertainty in the input data.
(2) Description of the Prior Art
Combat system information processing entails the integration of data from diverse sources for tactical picture generation and maintenance, situation assessment and planning, and allocation/control of resources. Current methods for data integration and decision support in submarine combat systems do not adequately account for uncertainty in the input data in an automated fashion. Instead they rely heavily on operator manipulation and human interpretation. On the other hand, in recent years the amount and flow rate of input data for integration has been rapidly increasing. It is anticipated that advances in sensor technology will continue to offer more possibilities in gathering both acoustic and non-acoustic data from organic as well as off-board sources, environmental and kinematic monitors, and intelligence reports. The combat system of the future therefore requires the ability to automatically manage uncertainty in the input data. Automated methods for handling uncertainty in the input data remains an outstanding technical issue and constitutes a significant Navy problem as well as a scientific and industrial challenge.
Uncertainty refers to being in a condition of doubt. This is contrasted to a condition of certainty or being definite, known, or specific. In an information processing context, uncertainty can be thought of as having a lack of definitive knowledge necessary to describe the process. Uncertainty in the input may result due to many causes including but certainly not limited to measurement noise, gaps in sensor information, sensor bias, inadequate number or placement of sensors, transmission noise or limitations, and the like. While most signals are measured within a tolerance, e.g., ten volts plus or minus one hundred micro volts, an uncertain signal is not known within the normal tolerances and may be so uncertain that normally used sensor tolerances are meaningless. Thus, while a tolerance of one hundred micro volts might be an accepted tolerance for an accurate signal in a particular application, an uncertain signal might vary by several volts or by more than one thousand times the normal accepted tolerance for the signal, thus making the signal quite uncertain in a particular application. Thus, a known or definite signal might be ten volts, an uncertain signal might be representable only as a possible value between eight and twelve volts. As another example of uncertain input, a sonar system working in a multipath environment may send out a sonar pulse and receive two or three sonar pulses in return. All three sonar pulses may be received within a time frame that would present reasonable distance/direction information for receipt from the intended target. Therefore, there is uncertainty associated with the acoustic propagathon path for each returned sonar pulse. As another example, it may be possible to get an approximate targeting solution value immediately since a decision for action may need to be taken now, whereas in time a more precise value will be available. This situation arises in a target motion analysis where a fundamental property of bearings-only target motion analysis is that contact range is not observable for a single-leg ownship motion (wherein a leg is defined as a time interval of constant platform velocity). The range becomes observable only after an ownship maneuver followed by a second leg of motion that therefore introduces a time-latency in the estimation process owing to the necessity of collecting sufficient data on all legs of motion. Thus, there are many different scenarios of types of uncertainty that will depend on each different situation.
As a general matter, an information processing system such as a combat control system or other typical control system will produce one or more specific or definite control signals in response to the input data. A representative example might include a tactical picture display that might show a submarine in relation to other targets. Another example might include a control for a motor to adjust rudder position. This is also true of a fuzzy logic-based control system. Fuzzy logic control systems have been employed successfully employed in various applications. Moreover, fuzzy logic controllers have been successfully applied and demonstrated in underwater combat control systems such as, for example, a conditioned fuzzy logic controller for an acoustic vehicle intercept guidance system.
A prior art fuzzy inference system has three basic components. The fuzzifier converts discrete or crisp input numbers to fuzzy logic membership values that describe a qualitative description of the discrete input in semantic terms. For instance, a numerical sensor value such as might be produced from a sensor voltage might be converted from its discrete, known, or specific values to a fuzzy logic membership value in a qualitative class, e.g., low, medium, or high. The output of the fuzzifier is represented in these membership values, and comprises the fuzzy input membership values. The fuzzifier is not designed to handle an input that is inexact and has a possibility of varying throughout a range of values.
The input membership values are used by an inference engine. The inference engine employs a knowledge base of rules that permit one or more inferences, and subsequent aggregation of all the output membership functions from the rules that are triggered by the fuzzy input membership values. Thus, the inference engine maps the fuzzy input membership values to a single fuzzy output set based on applicable rules from the knowledge base.
The defuzzifier converts the fuzzy output set to a crisp, discrete, particular output value for subsequent usage, e.g., the controller output in a feedback system. The crisp output is representative of the fuzzy output set and might be analogous to the expected value in a probability distribution.
In summary, a conventional fuzzy system does not have the mechanism to handle an uncertain input, yet such inputs are typically encountered in practice, e.g., data integration for submarine combat control. Simply taking an average, making an estimate, or calculating a normal value and using the discrete value so determined as input to the fuzzy logic inference system will limit the information that is available about the uncertainties, and thereby reduce the likelihood of making the best possible decision. Consequently, there remains a need for a fuzzy logic-based information processing system that can handle uncertain input. Those skilled in the art will appreciate the present invention that addresses the above and other problems.